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arxiv: 2604.27720 · v1 · submitted 2026-04-30 · 💻 cs.AI

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Auditing Frontier Vision-Language Models for Trustworthy Medical VQA: Grounding Failures, Format Collapse, and Domain Adaptation

Xupeng Chen , Binbin Shi , Chenqian Le , Qifu Yin , Lang Lin , Haowei Ni , Ran Gong , Panfeng Li
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Pith reviewed 2026-05-07 05:19 UTC · model grok-4.3

classification 💻 cs.AI
keywords medical VQAvision-language modelsgrounding failurestrustworthinessdomain adaptationlocalizationSLAKEVQA-RAD
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The pith

Frontier vision-language models localize anatomical targets poorly in medical VQA, with grounding quality as the primary trustworthiness bottleneck.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper audits five frontier VLMs on medical visual question answering using SLAKE and VQA-RAD datasets along perception and pipeline integration axes. All models localize targets inaccurately, with the best reaching only 0.23 mean IoU and 19.1% Acc@0.5, while also showing clinically dangerous laterality confusion. A self-grounding pipeline where the model localizes first then answers reduces VQA accuracy for every model, driven by inaccurate boxes and format parse failures rising to 70-99% for some models on VQA-RAD. Substituting predicted boxes with ground-truth annotations recovers and improves accuracy, showing the failure is in perception rather than decomposition. Supervised fine-tuning of Qwen 2.5 VL on combined medical VQA data reaches 85.5% open-ended recall on SLAKE, the highest reported for comparable methods.

Core claim

The authors establish that grounding quality is a primary trustworthiness bottleneck in the SLAKE bounding-box setting for frontier VLMs on medical VQA. Models exhibit poor localization of anatomical and pathological targets plus laterality confusion, and self-grounding pipelines degrade accuracy through both localization errors and format-compliance failures, while ground-truth boxes restore performance and supervised fine-tuning on medical data markedly improves VQA recall.

What carries the argument

The self-grounding pipeline, where the same VLM first predicts bounding boxes for targets then generates answers, evaluated using mean IoU and Acc@0.5 for localization quality plus parse failure rates and VQA accuracy on SLAKE and VQA-RAD.

If this is right

  • Inaccurate localization produces clinically dangerous laterality confusion across all tested models.
  • Self-grounding pipelines degrade VQA accuracy for every model through combined localization and format failures.
  • Replacing predicted boxes with ground-truth annotations recovers and improves VQA accuracy, isolating the problem to the perception module.
  • Supervised fine-tuning on combined Med-VQA training data achieves the highest reported SLAKE open-ended recall, indicating the answer-generation gap is tractable via domain adaptation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • It is unknown whether the same supervised fine-tuning also improves localization accuracy or reduces laterality errors.
  • Grounding failures in specialized domains may require hybrid systems that pair VLMs with dedicated detection modules rather than relying on self-grounding.
  • These perception bottlenecks could appear in other spatially precise VLM applications such as radiology report generation.

Load-bearing premise

The self-grounding pipeline and chosen datasets SLAKE and VQA-RAD represent realistic clinical integration challenges, and localization failures observed generalize to other medical VQA tasks.

What would settle it

A frontier VLM achieving mean IoU above 0.5 on SLAKE bounding boxes, preserving or improving VQA accuracy under self-grounding without format collapse, and showing no laterality confusion would falsify the grounding bottleneck claim.

Figures

Figures reproduced from arXiv: 2604.27720 by Binbin Shi, Chenqian Le, Haowei Ni, Lang Lin, Panfeng Li, Qifu Yin, Ran Gong, Xupeng Chen.

Figure 1
Figure 1. Figure 1: The two-step grounding-enhanced VQA pipeline. Step 1: the VLM jointly consumes the prompt, question, and medical image and emits a predicted ROI [x1, y1, x2, y2]; depending on the configuration, the ROI is used to crop the image (Self-/GT-Grounding) or the full image is forwarded directly (Direct VQA). Step 2: the same VLM is re-invoked with the selected input, the question, and a task instruction to produ… view at source ↗
Figure 2
Figure 2. Figure 2: Qualitative grounding for all five VLMs on two samples. Top: lung cancer in a CT scan—all models fail (IoU = 0.00). Bottom: cardiomegaly in a chest X-ray—models achieve partial overlap (IoU 0.48–0.69) but oversize the box. Green: ground truth; red dashed: model prediction view at source ↗
Figure 3
Figure 3. Figure 3: SLAKE VQA under three conditions for all five evaluated VLMs. Self-Grounding consistently degrades performance (red shaded zone); GT-Grounding recovers and surpasses Direct VQA (green shaded zone) for every model. (e.g., Qwen: 70.6%→65.9% closed on VQA-RAD, with only 0.2% parse failures). This pattern suggests that self￾grounding degradation is driven by both inaccurate local￾ization and format compliance … view at source ↗
Figure 4
Figure 4. Figure 4: Relationship between grounding quality (IoU, view at source ↗
read the original abstract

Deploying vision-language models (VLMs) in clinical settings demands auditable behavior under realistic failure conditions, yet the failure landscape of frontier VLMs on specialized medical inputs is poorly characterized. We audit five recent frontier and grounding-aware VLMs (Gemini~2.5~Pro, GPT-5, o3, GLM-4.5V, Qwen~2.5~VL) on Medical VQA along two trust-relevant axes. Perception: all models localize anatomical and pathological targets poorly -- the best model reaches only 0.23 mean IoU and 19.1% Acc@0.5 -- and exhibit clinically dangerous laterality confusion. Pipeline integration: a self-grounding pipeline, where the same model localizes then answers, degrades VQA accuracy for every model -- driven by both inaccurate localization and format-compliance failures under the two-step prompt (parse failure rises to 70%--99% for Gemini and GPT-5 on VQA-RAD). Replacing predicted boxes with ground-truth annotations recovers and improves VQA accuracy, consistent with the failure residing in the perception module rather than in the decomposition itself. These observational findings identify grounding quality as a primary trustworthiness bottleneck in our SLAKE bounding-box setting. As a complementary fine-tuning follow-up, supervised fine-tuning of Qwen~2.5~VL on combined Med-VQA training data attains the highest reported SLAKE open-ended recall (85.5%) among comparable methods, suggesting that the VQA-level gap is tractable with domain adaptation; whether this also closes the perception/trustworthiness bottleneck is left to future work.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 3 minor

Summary. The paper audits five frontier VLMs (Gemini 2.5 Pro, GPT-5, o3, GLM-4.5V, Qwen 2.5 VL) on medical VQA using SLAKE and VQA-RAD. It reports poor localization (best mean IoU 0.23, 19.1% Acc@0.5, laterality errors), shows self-grounding pipelines degrade VQA accuracy via inaccurate boxes and high format/parse failures (70-99% for some models), demonstrates that substituting ground-truth boxes recovers/improves VQA accuracy (isolating the perception bottleneck), and reports that supervised fine-tuning of Qwen 2.5 VL on combined Med-VQA data achieves 85.5% open-ended recall on SLAKE.

Significance. If the empirical results hold, the work provides concrete evidence that grounding quality is a primary trustworthiness bottleneck for frontier VLMs in medical VQA, at least in the SLAKE bounding-box setting. The recovery experiment directly supports the perception-module diagnosis over pipeline-decomposition issues. The fine-tuning result shows VQA-level gaps are addressable via domain adaptation, though perception closure is left open. This adds reproducible observational data on failure modes (localization, laterality, format collapse) that can inform safer deployment and future auditing protocols.

major comments (3)
  1. [§4] §4 (Localization and IoU results): the mean IoU of 0.23 and Acc@0.5 of 19.1% for the best model are central to the perception-bottleneck claim, yet the manuscript must specify the exact box-extraction method from free-form VLM outputs, any thresholding or post-processing, and whether IoU is computed only on successfully parsed boxes or all cases.
  2. [§5] §5 (Self-grounding pipeline and format failures): the reported 70-99% parse-failure rates for Gemini and GPT-5 on VQA-RAD are load-bearing for the pipeline-degradation analysis; the paper should provide the precise prompt templates for the two-step process, the parsing regex or LLM-based extractor used, and an ablation showing VQA accuracy when format failures are manually corrected versus localization errors.
  3. [Recovery experiment] Recovery experiment (main results): while substituting ground-truth boxes restores VQA accuracy, the manuscript needs to report per-model accuracy deltas with standard errors or p-values from paired tests, and confirm that the answer-generation stage remains identical (same prompt, temperature, etc.) so the isolation to perception is unambiguous.
minor comments (3)
  1. [Abstract] Abstract and §2: the list of models is described as 'grounding-aware,' but o3 and GPT-5 are not natively grounding models; clarify whether grounding is achieved via tool use or special prompting and whether this affects the comparison.
  2. [Fine-tuning] Fine-tuning section: the 85.5% open-ended recall is stated as the highest reported; include a comparison table against prior Med-VQA methods with identical metrics, dataset splits, and references to allow direct assessment of the improvement.
  3. [§3] Dataset choice: SLAKE and VQA-RAD are used; briefly justify why these particular datasets (with their bounding-box annotations) are representative of the clinical integration challenges mentioned in the introduction.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments highlight important aspects of reproducibility and statistical rigor that strengthen the manuscript. We have revised the paper to address each point, adding the requested methodological specifications, prompt details, parsing procedures, an ablation isolating format versus localization errors, and per-model statistical results for the recovery experiment. Below we respond to the major comments.

read point-by-point responses

  1. Referee: [§4] §4 (Localization and IoU results): the mean IoU of 0.23 and Acc@0.5 of 19.1% for the best model are central to the perception-bottleneck claim, yet the manuscript must specify the exact box-extraction method from free-form VLM outputs, any thresholding or post-processing, and whether IoU is computed only on successfully parsed boxes or all cases.

    Authors: We agree that these implementation details are necessary for full reproducibility. In the revised §4 we now describe the box-extraction procedure in full: a regular-expression pattern is first applied to identify candidate coordinate tuples in the free-form VLM output; valid four-tuples are parsed into [x1, y1, x2, y2] format after normalization to image dimensions. Outputs that fail to yield a valid tuple (including malformed text or missing coordinates) are treated as empty boxes. No confidence thresholding or additional post-processing is applied. IoU is computed over the entire test set, with parse failures contributing an IoU of zero; this choice is now stated explicitly in the section and in the table captions. These clarifications do not alter the reported numbers but make the evaluation protocol transparent. revision: yes

  2. Referee: [§5] §5 (Self-grounding pipeline and format failures): the reported 70-99% parse-failure rates for Gemini and GPT-5 on VQA-RAD are load-bearing for the pipeline-degradation analysis; the paper should provide the precise prompt templates for the two-step process, the parsing regex or LLM-based extractor used, and an ablation showing VQA accuracy when format failures are manually corrected versus localization errors.

    Authors: We have added the complete two-step prompt templates to Appendix B and described the parsing pipeline in the revised §5. Extraction begins with regex matching for structured coordinate strings; any remaining free-form responses are passed to a deterministic LLM-based extractor (temperature 0) that returns only the bounding-box field or an explicit failure token. We have also inserted a new ablation table that reports VQA accuracy under three conditions: (i) original self-grounding outputs, (ii) format failures manually corrected while retaining the model’s predicted (inaccurate) boxes, and (iii) ground-truth boxes. The results show that correcting format alone recovers only a modest fraction of the accuracy drop, confirming that localization error remains the dominant factor. This ablation uses the same answer-generation prompt and sampling parameters as the main experiments. revision: yes

  3. Referee: [Recovery experiment] Recovery experiment (main results): while substituting ground-truth boxes restores VQA accuracy, the manuscript needs to report per-model accuracy deltas with standard errors or p-values from paired tests, and confirm that the answer-generation stage remains identical (same prompt, temperature, etc.) so the isolation to perception is unambiguous.

    Authors: We have expanded the recovery-experiment subsection to include per-model accuracy deltas together with bootstrap standard errors (1,000 resamples) and paired t-test p-values. All models show statistically significant gains (p < 0.01) when ground-truth boxes replace predicted boxes. We now explicitly state that the answer-generation stage is held constant: identical prompt wording, temperature = 0, and maximum-token limit are used in both the original and recovery runs, ensuring that the observed accuracy recovery isolates the effect of the perception (box) input. These additions appear in the main results table and accompanying text. revision: yes

Circularity Check

0 steps flagged

No significant circularity: purely empirical audit with direct observations

full rationale

The paper reports direct experimental measurements on public frontier VLMs and standard datasets (SLAKE, VQA-RAD). Central claims rest on two observable results: (1) low localization performance (0.23 mean IoU, laterality errors) and (2) recovery of VQA accuracy when ground-truth boxes replace model predictions. These are isolated by the GT-substitution experiment itself, without any equations, fitted parameters, self-citations that bear load, or definitional reductions. The fine-tuning result is likewise a reported performance number on held-out data. No derivation chain exists that could collapse to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

This is an empirical audit study with no mathematical derivations or fitted parameters. It relies on two domain assumptions about benchmark validity and pipeline relevance rather than introducing new entities or free parameters.

axioms (2)
  • domain assumption The chosen medical VQA datasets and bounding-box annotations are representative of clinical perception tasks.
    Invoked when generalizing localization failures to trustworthiness bottlenecks in medical settings.
  • domain assumption The self-grounding pipeline (localize then answer) is a relevant test of real-world pipeline integration.
    Used to attribute VQA degradation to perception rather than decomposition.

pith-pipeline@v0.9.0 · 5622 in / 1525 out tokens · 95730 ms · 2026-05-07T05:19:57.745142+00:00 · methodology

discussion (0)

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